This IT guy could save your life

K. P. Unnikrishnan is director of neurological research informatics at NorthShore. "I'm like a kid in a candy store."

It's common practice for banks, supermarkets and online retailers to mine the data they collect on customers to rethink marketing campaigns, make targeted offers and keep inventories up to date. It's far less common in health care, where much of the useful data lies trapped in paper records and notes, often scribbled in barely decipherable script.

The relatively slow pace with which many physicians and hospitals have been moving this information into electronic systems creates a competitive advantage for front-runners like NorthShore University HealthSystem, Northwestern Memorial Hospital and Northwestern Memorial Faculty Foundation, which invested early in health information technology. They are now primed to mine their electronic medical records to identify patients whose genetic makeup puts them at high risk of developing diseases or predict hours ahead of time whether a hospitalized patient is likely to have a heart attack.

To speed its data-mining push, NorthShore has invested $7 million to launch its Center for Clinical and Research Informatics and has hired a group of 20 physicians, computer scientists and statisticians with expertise in predictive modeling to dig for clues about the patients most likely to suffer complications from pancreatic surgery or be readmitted to the hospital. In less than 16 months, they have launched more than 50 projects, including one that helps the system identify patients with a likelihood of arriving at one of its hospitals carrying Methicillin-resistant Staphylococcus aureus, a difficult-to-treat staph infection that does not respond to some antibiotics.

Rather than testing everyone who is admitted, as the health system has done in the past, it is now targeting those whom the predictive model suggests may be carriers.

“That allows us to spend about half the money on the epidemiological testing that we would otherwise need to spend,” says Jonathan Silverstein, head of the center. Dr. Silverstein estimates this alone will save the system at least $500,000 per year.

Another algorithm is helping identify patients in primary care practices who are suffering from undiagnosed hypertension. It does so by piecing together discrete pieces of data—a blood pressure reading taken in the emergency department, another in the hospital and a third taken in a physician's offices. If a problem exists, the system sends an alert to the physician.

K.P. Unnikrishnan, director of neurological research informatics at NorthShore and a recent recruit to the center, spent nearly 20 years at General Motors Co., where he used data-mining techniques to predict failures in the assembly line and cars. He says he was drawn by NorthShore's trove of data: “I'm like a kid in a candy store.”

DATA DELUGE

Across town, faculty members at Northwestern University's Feinberg School of Medicine have been working to identify genetic variants that contribute to disease susceptibility or the success or failure of therapies. So far, approximately 11,000 patients have contributed their genetic information to the system and have granted access to electronic medical records, enabling researchers to tie genomic information to disease characteristics. This data is combined with that of eight other institutions that are participating in the Electronic Medical Records and Genomics network, or eMerge, a National Institutes of Health-funded collaborative that has helped to identify genetic variants associated with dementia, cataracts and Type 2 diabetes.

Northwestern received a grant of roughly $7 million from the National Institutes of Health to pursue this work, which has yielded predictive models Northwestern Memorial Hospital and the Northwestern Memorial Faculty Foundation have begun to deploy. One model uses lab results, body mass index measures and medication records, among other markers, to find patients with undiagnosed Type 2 diabetes. “A human could (do this), but it would be very time-consuming and you couldn't do it across all 2.2 million people in our electronic health record system. A computer can,” says Rex Chisholm, vice dean for scientific affairs and graduate education at the medical school and chair of the national eMerge network's steering committee. Because the program is funded with federal dollars, the algorithms will be available for other institutions. “The goal is to create a public library,” he says.

Both institutions are anticipating receiving significant research dollars from the federal government, which at least for now has billions to invest in comparative effectiveness research, a provision of the health care reform law.

Over time, more health systems are likely to join these efforts, which were once led by insurance companies using their vast claims data to identify effective patterns of care. “Originally health plans were the driving force, but you do see it being used more and more in health systems,” says Clive Riddle, publisher of Predictive Modeling News, a Modesto, Calif.-based newsletter for health professionals involved in predictive modeling.

Claims data from payers, which can help distinguish physicians who adhere to evidence-based standards of care from those who don't, will still play a significant role in improving patient outcomes. Chicago-based Blue Health Intelligence, a data analytics firm and licensee of Blue Cross and Blue Shield Association, brings together claims data on patients in 18 Blues plans across the nation to identify health trends and look for patterns of care that lead to better outcomes. This enables the Blues plans to benchmark their performance against one another and see why care is more efficient in some locations. The goal is to identify the patterns in the care that lead to better outcomes and share that, says Swati Abbott, Blue Health's CEO.

Mr. Chisholm has similar dreams. “I fantasize about using data mining to look for patterns we don't know about,” he says. Among these might be correlations between diseases that we now think of as discrete problems. “As data mining processes get better and better, you can go back . . . (and) find new things you haven't seen before,” he says.

(Editor's note: Mr. Silverstein's first name has been corrected in this updated version.)